|
|
|
|
|
|
| Detection of Pb Contents in Soil Using LIBS Coupled With Univariate Calibration Curve Methods |
| LUO Wei, TIAN Peng, DONG Wen-tao, HUANG Yi-feng, LIU Xue-mei, ZHAN Bai-shao, ZHANG Hai-liang* |
| East China Jiaotong University, Nanchang 330013, China |
|
|
|
|
Abstract Heavy metal (like Pb, Cd, Cu, et al) in soil has affected the human health for a long time. So, the detection and prevention of soil Pb content have been a hot topic at home and abroad. Traditional methods of soil heavy metal detection, such as atomic absorption spectrometry (AAS), X fluorescence spectrometry (XRFS), are high cost, complicated, time-consuming, cannot meet the requirements of rapid analysis, and is easy to form secondary pollution of samples. Laser-induced breakdown spectroscopy (LIBS), a typical atomic emission spectrum, is a combination of laser technology and spectroscopic technology. It is based on the analysis of characteristic spectral line information that is excited to emit atoms and ions in a substance, and then compositions of the substance were studied. LIBS technology can rapidly detect the composition and content of material elements in any state (solid, liquid and gaseous). It is regarded as an emerging technology in the field of future chemical detection and rapid green analysis. LIBS technology has the advantages of simple pre-processing (or no processing required) for samples, multi-element simultaneous analysis, long-distance measurement, and wide applicability. Based on those advantages, it is widely used in various fields and viewed as one of the research hotspots. Under the background of agricultural informatization, the elements of Pb in the soil will be considered as the research carriers. And laser-induced breakdown spectroscopy (LIBS) technique combined with theoretical analysis and mathematical modeling will be employed to accurately detect the contents of Pb content. Then, the univariate calibration curve methods were built to predict heavy metal Pb content. Firstly, 15 soil samples with known Pb concentration gradient were selected for analysis. Soil LIBS spectral data were pretreated with different pre-processing methods. Three models based on LIBS intensity, peak areas, Lorentz fit intensity after normalized corresponding Pb content was established and fitted to analyze Pb content in soil quantitatively. The results show that the R2 of soil Pb content prediction based on three calibration curve models are 0.918 0, 0.910 1 and 0.914 3, respectively. The results of the three calibration curve analysis methods are good. It indicates that LIBS combined with univariate calibration curve method showed high reliability in detecting Pb in soil. The research results provide the theoretical foundation for developing the diagnosis and prevention technology of heavy metal contamination in soil and offer technical support for scientific spraying and precision management in agricultural production.
|
|
Received: 2020-02-05
Accepted: 2020-05-16
|
|
|
|
Corresponding Authors:
ZHANG Hai-liang
E-mail: hailiang.zhang@163.com
|
|
[1] Zwolak A, Sarzyńska M, Szpyrka E, et al. Water Air and Soil Pollution, 2019, 230(7): 164.
[2] Hahn D W, Omenetto N. Applied Spectroscopy, 2012, 66(4): 347.
[3] Salam F, Puat N H A, Rahman G A, et al. Procedia Chemistry, 2016, 20: 33.
[4] Cremers D A, Chinni R C. Applied Spectroscopy Reviews, 2009, 44(6): 457.
[5] Hahn D W, Omenetto N. Applied Spectroscopy, 2010, 64(12): 335A.
[6] Skol A D, Scott L J, Abecasis G R, et al. Nature Genetics, 2006, 38(2): 209.
[7] Hahn D W, Omenetto N. Applied Spectroscopy, 2012, 66(4): 347.
[8] Khan S A, Ibrahim M, Jamil Y, et al. Journal of Chemistry, 2013, 2013: 894020(http://dx.doi.org/10.1155/2013/894020).
[9] Yu K, Zhao Y, He Y, et al. Chemometrics and Intelligent Laboratory Systems, 2019, 195: 103891(10.1016/j.chemolab.2019.103891).
[10] NIST. “Atomic Spectra Database Lines Form”. http://physics.nist.gov/PhysRefData/ASD/lines_form.html.
[11] Wang J, Shi M, Zheng P, et al. Journal of Applied Spectroscopy, 2018, 85(1): 190.
[12] Fortes F J, Moros J, Lucena P, et al. Analytical Chemistry, 2013, 85(2): 640.
[13] Li Y, Lu Y, Lan Y, et al. Applied Optics, 2018, 57(13): 3539.
[14] Sobron P, Wang A, Sobron F. Spectrochimica Acta Part B, 2012, 68: 1. |
| [1] |
LIU Jia1, 2, GUO Fei-fei2, YU Lei2, CUI Fei-peng2, ZHAO Ying2, HAN Bing2, SHEN Xue-jing1, 2, WANG Hai-zhou1, 2*. Quantitative Characterization of Components in Neodymium Iron Boron Permanent Magnets by Laser Induced Breakdown Spectroscopy （LIBS）[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 141-147. |
| [2] |
YANG Wen-feng1, LIN De-hui1, CAO Yu2, QIAN Zi-ran1, LI Shao-long1, ZHU De-hua2, LI Guo1, ZHANG Sai1. Study on LIBS Online Monitoring of Aircraft Skin Laser Layered Paint Removal Based on PCA-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3891-3898. |
| [3] |
SUN Cheng-yu1, JIAO Long1*, YAN Na-ying1, YAN Chun-hua1, QU Le2, ZHANG Sheng-rui3, MA Ling1. Identification of Salvia Miltiorrhiza From Different Origins by Laser
Induced Breakdown Spectroscopy Combined with Artificial Neural
Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3098-3104. |
| [4] |
LIU Shu1, JIN Yue1, 2, SU Piao1, 2, MIN Hong1, AN Ya-rui2, WU Xiao-hong1*. Determination of Calcium, Magnesium, Aluminium and Silicon Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy Assisted by Variable Importance-Back Propagation Artificial Neural Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3132-3142. |
| [5] |
LI Chang-ming1, CHEN An-min2*, GAO Xun3*, JIN Ming-xing2. Spatially Resolved Laser-Induced Plasma Spectroscopy Under Different Sample Temperatures[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2032-2036. |
| [6] |
ZHAO Yang1, ZHANG Lei2, 3*, CHENG Nian-kai4, YIN Wang-bao2, 3*, HOU Jia-jia5, BAI Cheng-hua1. Research on Space-Time Evolutionary Mechanisms of Species Distribution in Laser Induced Binary Plasma[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2067-2073. |
| [7] |
WANG Bin1, 2, ZHENG Shao-feng2, GAN Jiu-lin1, LIU Shu3, LI Wei-cai2, YANG Zhong-min1, SONG Wu-yuan4*. Plastic Reference Material (PRM) Combined With Partial Least Square (PLS) in Laser-Induced Breakdown Spectroscopy (LIBS) in the Field of Quantitative Elemental Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2124-2131. |
| [8] |
HU Meng-ying1, 2, ZHANG Peng-peng1, 2, LIU Bin1, 2, DU Xue-miao1, 2, ZHANG Ling-huo1, 2, XU Jin-li1, 2*, BAI Jin-feng1, 2. Determination of Si, Al, Fe, K in Soil by High Pressure Pelletised Sample and Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2174-2180. |
| [9] |
WU Shu-jia1, 2, YAO Ming-yin2, 3, ZENG Jian-hui2, HE Liang2, FU Gang-rong2, ZENG Yu-qi2, XUE Long2, 3, LIU Mu-hua2, 3, LI Jing2, 3*. Laser-Induced Breakdown Spectroscopy Detection of Cu Element in Pig Fodder by Combining Cavity-Confinement[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1770-1775. |
| [10] |
YUAN Shu, WU Ding*, WU Hua-ce, LIU Jia-min, LÜ Yan, HAI Ran, LI Cong, FENG Chun-lei, DING Hong-bin. Study on the Temporal and Spatial Evolution of Optical Emission From the Laser Induced Multi-Component Plasma of Tungsten Carbide Copper Alloy in Vacuum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1394-1400. |
| [11] |
WANG Qiu, LI Bin, HAN Zhao-yang, ZHAN Chao-hui, LIAO Jun, LIU Yan-de*. Research on Anthracnose Grade of Camellia Oleifera Based on the Combined LIBS and Fourier Transform NIR Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1450-1458. |
| [12] |
CHAI Shu1, PENG Hai-meng1, WU Wen-dong1, 2*. Acoustic-Based Spectral Correction Method for Laser-Induced Breakdown Spectroscopy in High Temperature Environment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1401-1407. |
| [13] |
NING Qian-qian, YANG Jia-hao, LIU Xiao-lin, HE Yu-han, HUANGFU Zhi-chao, YU Wen-jing, WANG Zhao-hui*. Design and Study of Time-Resolved Femtosecond Laser-Induced
Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1083-1087. |
| [14] |
DING Kun-yan1, HE Chang-tao2, LIU Zhi-gang2*, XIAO Jing1, FENG Guo-ying1, ZHOU Kai-nan3, XIE Na3, HAN Jing-hua1. Research on Particulate Contamination Induced Laser Damage of Optical Material Based on Integrated Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1234-1241. |
| [15] |
SU Yun-peng, HE Chun-jing, LI Ang-ze, XU Ke-mi, QIU Li-rong, CUI Han*. Ore Classification and Recognition Based on Confocal LIBS Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 692-697. |
|
|
|
|
